Indexed by:会议论文
Date of Publication:2017-01-01
Included Journals:EI、CPCI-S
Volume:10559
Page Number:390-399
Key Words:Saliency detection; Logistic regression; Label propagation; Affinity learning
Abstract:This paper introduces a novel saliency detection method by incorporating logistic regression into the label propagation framework, along with a principled weight computation for saliency fusion. First, the initial map is generated by computing objectness and backgroundness. Second, we unify logistic regression and label propagation to predict saliency labels. Last, we fuse the predicted result and initial map and further refine the fused map across multiple scales. Moreover, we use clustering random forest to learn the pairwise affinities between super-pixels for backgroundness computation and saliency prediction. Extensive experiments on three large benchmark datasets demonstrate the proposed algorithm performs well against the state-of-the-art methods.
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